Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations18207
Missing cells878
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.7 MiB
Average record size in memory498.7 B

Variable types

Numeric10
Text3
Categorical4
DateTime1

Alerts

Age is highly overall correlated with IDHigh correlation
Height is highly overall correlated with WeightHigh correlation
ID is highly overall correlated with Age and 1 other fieldsHigh correlation
International Reputation is highly overall correlated with Overall and 1 other fieldsHigh correlation
Overall is highly overall correlated with ID and 5 other fieldsHigh correlation
Position is highly overall correlated with Skill MovesHigh correlation
Potential is highly overall correlated with Overall and 3 other fieldsHigh correlation
Release Clause is highly overall correlated with Overall and 3 other fieldsHigh correlation
Skill Moves is highly overall correlated with PositionHigh correlation
Value is highly overall correlated with Overall and 3 other fieldsHigh correlation
Wage is highly overall correlated with International Reputation and 4 other fieldsHigh correlation
Weight is highly overall correlated with HeightHigh correlation
International Reputation is highly imbalanced (77.7%) Imbalance
Club has 241 (1.3%) missing values Missing
Value has 252 (1.4%) missing values Missing
Contract Valid Until has 289 (1.6%) missing values Missing
ID has unique values Unique
Wage has 241 (1.3%) zeros Zeros

Reproduction

Analysis started2025-01-02 16:28:42.537446
Analysis finished2025-01-02 16:29:11.341206
Duration28.8 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

High correlation  Unique 

Distinct18207
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214298.34
Minimum16
Maximum246620
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.4 KiB
2025-01-02T16:29:11.537685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile163964
Q1200315.5
median221759
Q3236529.5
95-th percentile244735.7
Maximum246620
Range246604
Interquartile range (IQR)36214

Descriptive statistics

Standard deviation29965.244
Coefficient of variation (CV)0.13982957
Kurtosis9.6094996
Mean214298.34
Median Absolute Deviation (MAD)17325
Skewness-2.2679827
Sum3.9017299 × 109
Variance8.9791586 × 108
MonotonicityNot monotonic
2025-01-02T16:29:11.856619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
158023 1
 
< 0.1%
245008 1
 
< 0.1%
169489 1
 
< 0.1%
232465 1
 
< 0.1%
228625 1
 
< 0.1%
227857 1
 
< 0.1%
225809 1
 
< 0.1%
209425 1
 
< 0.1%
172560 1
 
< 0.1%
199442 1
 
< 0.1%
Other values (18197) 18197
99.9%
ValueCountFrequency (%)
16 1
< 0.1%
41 1
< 0.1%
80 1
< 0.1%
164 1
< 0.1%
657 1
< 0.1%
768 1
< 0.1%
1179 1
< 0.1%
2147 1
< 0.1%
2335 1
< 0.1%
2702 1
< 0.1%
ValueCountFrequency (%)
246620 1
< 0.1%
246617 1
< 0.1%
246616 1
< 0.1%
246613 1
< 0.1%
246609 1
< 0.1%
246608 1
< 0.1%
246606 1
< 0.1%
246603 1
< 0.1%
246602 1
< 0.1%
246601 1
< 0.1%

Name
Text

Distinct17194
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-01-02T16:29:12.376491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length22
Median length20
Mean length10.018235
Min length2

Characters and Unicode

Total characters182402
Distinct characters136
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16432 ?
Unique (%)90.3%

Sample

1st rowL. Messi
2nd rowCristiano Ronaldo
3rd rowNeymar Jr
4th rowDe Gea
5th rowK. De Bruyne
ValueCountFrequency (%)
m 1775
 
4.9%
j 1630
 
4.5%
a 1529
 
4.2%
s 1024
 
2.8%
d 945
 
2.6%
l 777
 
2.1%
c 766
 
2.1%
r 764
 
2.1%
f 609
 
1.7%
t 602
 
1.6%
Other values (13612) 26115
71.5%
2025-01-02T16:29:13.224255image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18329
 
10.0%
. 15182
 
8.3%
a 14163
 
7.8%
e 11538
 
6.3%
o 9742
 
5.3%
i 9544
 
5.2%
n 9283
 
5.1%
r 8955
 
4.9%
l 6525
 
3.6%
s 5471
 
3.0%
Other values (126) 73670
40.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182402
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18329
 
10.0%
. 15182
 
8.3%
a 14163
 
7.8%
e 11538
 
6.3%
o 9742
 
5.3%
i 9544
 
5.2%
n 9283
 
5.1%
r 8955
 
4.9%
l 6525
 
3.6%
s 5471
 
3.0%
Other values (126) 73670
40.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182402
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18329
 
10.0%
. 15182
 
8.3%
a 14163
 
7.8%
e 11538
 
6.3%
o 9742
 
5.3%
i 9544
 
5.2%
n 9283
 
5.1%
r 8955
 
4.9%
l 6525
 
3.6%
s 5471
 
3.0%
Other values (126) 73670
40.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182402
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18329
 
10.0%
. 15182
 
8.3%
a 14163
 
7.8%
e 11538
 
6.3%
o 9742
 
5.3%
i 9544
 
5.2%
n 9283
 
5.1%
r 8955
 
4.9%
l 6525
 
3.6%
s 5471
 
3.0%
Other values (126) 73670
40.4%

Age
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.122206
Minimum16
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.4 KiB
2025-01-02T16:29:13.533326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile18
Q121
median25
Q328
95-th percentile33
Maximum45
Range29
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.6699427
Coefficient of variation (CV)0.18588904
Kurtosis-0.45951355
Mean25.122206
Median Absolute Deviation (MAD)4
Skewness0.39176414
Sum457400
Variance21.808365
MonotonicityNot monotonic
2025-01-02T16:29:13.794583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
21 1423
 
7.8%
26 1387
 
7.6%
24 1358
 
7.5%
22 1340
 
7.4%
23 1332
 
7.3%
25 1319
 
7.2%
20 1240
 
6.8%
27 1162
 
6.4%
28 1101
 
6.0%
19 1024
 
5.6%
Other values (19) 5521
30.3%
ValueCountFrequency (%)
16 42
 
0.2%
17 289
 
1.6%
18 732
4.0%
19 1024
5.6%
20 1240
6.8%
21 1423
7.8%
22 1340
7.4%
23 1332
7.3%
24 1358
7.5%
25 1319
7.2%
ValueCountFrequency (%)
45 1
 
< 0.1%
44 2
 
< 0.1%
42 1
 
< 0.1%
41 5
 
< 0.1%
40 13
 
0.1%
39 25
 
0.1%
38 37
 
0.2%
37 82
0.5%
36 127
0.7%
35 196
1.1%
Distinct164
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2025-01-02T16:29:14.262508image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length20
Median length18
Mean length7.6247597
Min length4

Characters and Unicode

Total characters138824
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.1%

Sample

1st rowArgentina
2nd rowPortugal
3rd rowBrazil
4th rowSpain
5th rowBelgium
ValueCountFrequency (%)
england 1662
 
7.9%
germany 1198
 
5.7%
spain 1072
 
5.1%
argentina 937
 
4.5%
france 914
 
4.3%
brazil 827
 
3.9%
republic 805
 
3.8%
italy 702
 
3.3%
colombia 618
 
2.9%
japan 478
 
2.3%
Other values (181) 11825
56.2%
2025-01-02T16:29:15.090905image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 18517
 
13.3%
n 13782
 
9.9%
e 11179
 
8.1%
r 9371
 
6.8%
i 9290
 
6.7%
l 8454
 
6.1%
o 5461
 
3.9%
t 5077
 
3.7%
d 4931
 
3.6%
g 4088
 
2.9%
Other values (47) 48674
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 138824
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 18517
 
13.3%
n 13782
 
9.9%
e 11179
 
8.1%
r 9371
 
6.8%
i 9290
 
6.7%
l 8454
 
6.1%
o 5461
 
3.9%
t 5077
 
3.7%
d 4931
 
3.6%
g 4088
 
2.9%
Other values (47) 48674
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 138824
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 18517
 
13.3%
n 13782
 
9.9%
e 11179
 
8.1%
r 9371
 
6.8%
i 9290
 
6.7%
l 8454
 
6.1%
o 5461
 
3.9%
t 5077
 
3.7%
d 4931
 
3.6%
g 4088
 
2.9%
Other values (47) 48674
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 138824
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 18517
 
13.3%
n 13782
 
9.9%
e 11179
 
8.1%
r 9371
 
6.8%
i 9290
 
6.7%
l 8454
 
6.1%
o 5461
 
3.9%
t 5077
 
3.7%
d 4931
 
3.6%
g 4088
 
2.9%
Other values (47) 48674
35.1%

Overall
Real number (ℝ)

High correlation 

Distinct48
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.238699
Minimum46
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.4 KiB
2025-01-02T16:29:15.443655image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile54
Q162
median66
Q371
95-th percentile77
Maximum94
Range48
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.9089296
Coefficient of variation (CV)0.10430352
Kurtosis0.096568667
Mean66.238699
Median Absolute Deviation (MAD)4
Skewness0.067184922
Sum1206008
Variance47.733308
MonotonicityDecreasing
2025-01-02T16:29:15.746715image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
66 1163
 
6.4%
67 1118
 
6.1%
64 1091
 
6.0%
65 1045
 
5.7%
68 1035
 
5.7%
63 1002
 
5.5%
69 973
 
5.3%
70 889
 
4.9%
62 878
 
4.8%
71 783
 
4.3%
Other values (38) 8230
45.2%
ValueCountFrequency (%)
46 1
 
< 0.1%
47 20
 
0.1%
48 32
 
0.2%
49 36
 
0.2%
50 103
 
0.6%
51 125
0.7%
52 159
0.9%
53 199
1.1%
54 250
1.4%
55 265
1.5%
ValueCountFrequency (%)
94 2
 
< 0.1%
92 1
 
< 0.1%
91 6
 
< 0.1%
90 5
 
< 0.1%
89 11
 
0.1%
88 17
 
0.1%
87 13
 
0.1%
86 22
0.1%
85 33
0.2%
84 45
0.2%

Potential
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.307299
Minimum48
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.4 KiB
2025-01-02T16:29:16.086946image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q167
median71
Q375
95-th percentile82
Maximum95
Range47
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.1364956
Coefficient of variation (CV)0.086057047
Kurtosis0.035825808
Mean71.307299
Median Absolute Deviation (MAD)4
Skewness0.2661536
Sum1298292
Variance37.656578
MonotonicityNot monotonic
2025-01-02T16:29:16.564965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
70 1203
 
6.6%
69 1175
 
6.5%
71 1140
 
6.3%
68 1136
 
6.2%
72 1122
 
6.2%
73 1051
 
5.8%
74 1015
 
5.6%
66 996
 
5.5%
67 991
 
5.4%
75 951
 
5.2%
Other values (37) 7427
40.8%
ValueCountFrequency (%)
48 2
 
< 0.1%
50 2
 
< 0.1%
51 2
 
< 0.1%
52 10
 
0.1%
53 6
 
< 0.1%
54 7
 
< 0.1%
55 18
 
0.1%
56 24
 
0.1%
57 47
0.3%
58 87
0.5%
ValueCountFrequency (%)
95 1
 
< 0.1%
94 3
 
< 0.1%
93 4
 
< 0.1%
92 9
 
< 0.1%
91 12
 
0.1%
90 21
 
0.1%
89 33
0.2%
88 48
0.3%
87 61
0.3%
86 82
0.5%

Club
Text

Missing 

Distinct651
Distinct (%)3.6%
Missing241
Missing (%)1.3%
Memory size1.3 MiB
2025-01-02T16:29:17.133577image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length35
Median length25
Mean length13.442836
Min length3

Characters and Unicode

Total characters241514
Distinct characters103
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFC Barcelona
2nd rowJuventus
3rd rowParis Saint-Germain
4th rowManchester United
5th rowManchester City
ValueCountFrequency (%)
fc 2860
 
7.3%
de 765
 
2.0%
club 638
 
1.6%
united 542
 
1.4%
city 541
 
1.4%
al 414
 
1.1%
cd 391
 
1.0%
atlético 380
 
1.0%
town 324
 
0.8%
cf 318
 
0.8%
Other values (936) 31840
81.6%
2025-01-02T16:29:18.327940image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21076
 
8.7%
a 19108
 
7.9%
e 18850
 
7.8%
n 14540
 
6.0%
o 13417
 
5.6%
r 13217
 
5.5%
i 12655
 
5.2%
l 10818
 
4.5%
t 10313
 
4.3%
s 8691
 
3.6%
Other values (93) 98829
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 241514
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21076
 
8.7%
a 19108
 
7.9%
e 18850
 
7.8%
n 14540
 
6.0%
o 13417
 
5.6%
r 13217
 
5.5%
i 12655
 
5.2%
l 10818
 
4.5%
t 10313
 
4.3%
s 8691
 
3.6%
Other values (93) 98829
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 241514
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21076
 
8.7%
a 19108
 
7.9%
e 18850
 
7.8%
n 14540
 
6.0%
o 13417
 
5.6%
r 13217
 
5.5%
i 12655
 
5.2%
l 10818
 
4.5%
t 10313
 
4.3%
s 8691
 
3.6%
Other values (93) 98829
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 241514
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21076
 
8.7%
a 19108
 
7.9%
e 18850
 
7.8%
n 14540
 
6.0%
o 13417
 
5.6%
r 13217
 
5.5%
i 12655
 
5.2%
l 10818
 
4.5%
t 10313
 
4.3%
s 8691
 
3.6%
Other values (93) 98829
40.9%

Value
Real number (ℝ)

High correlation  Missing 

Distinct216
Distinct (%)1.2%
Missing252
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean2444.5302
Minimum10
Maximum118500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.4 KiB
2025-01-02T16:29:18.874633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile100
Q1325
median700
Q32100
95-th percentile10000
Maximum118500
Range118490
Interquartile range (IQR)1775

Descriptive statistics

Standard deviation5626.7154
Coefficient of variation (CV)2.3017574
Kurtosis76.003054
Mean2444.5302
Median Absolute Deviation (MAD)490
Skewness7.0316107
Sum43891540
Variance31659927
MonotonicityNot monotonic
2025-01-02T16:29:19.467514image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100 431
 
2.4%
375 372
 
2.0%
425 354
 
1.9%
325 351
 
1.9%
450 343
 
1.9%
525 338
 
1.9%
350 325
 
1.8%
1200 324
 
1.8%
400 323
 
1.8%
1000 318
 
1.7%
Other values (206) 14476
79.5%
ValueCountFrequency (%)
10 15
 
0.1%
20 21
 
0.1%
30 23
 
0.1%
40 65
 
0.4%
50 127
0.7%
60 150
0.8%
70 139
0.8%
80 114
0.6%
90 136
0.7%
100 178
1.0%
ValueCountFrequency (%)
118500 1
< 0.1%
110500 1
< 0.1%
102000 1
< 0.1%
93000 1
< 0.1%
89000 1
< 0.1%
83500 1
< 0.1%
81000 1
< 0.1%
80000 1
< 0.1%
78000 1
< 0.1%
77000 2
< 0.1%

Wage
Real number (ℝ)

High correlation  Zeros 

Distinct144
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7313121
Minimum0
Maximum565
Zeros241
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size142.4 KiB
2025-01-02T16:29:20.201597image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q39
95-th percentile39
Maximum565
Range565
Interquartile range (IQR)8

Descriptive statistics

Standard deviation21.99929
Coefficient of variation (CV)2.2606705
Kurtosis100.71241
Mean9.7313121
Median Absolute Deviation (MAD)2
Skewness7.9060189
Sum177178
Variance483.96878
MonotonicityNot monotonic
2025-01-02T16:29:20.500244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4900
26.9%
2 2827
15.5%
3 1857
 
10.2%
4 1255
 
6.9%
5 869
 
4.8%
6 682
 
3.7%
7 488
 
2.7%
8 423
 
2.3%
9 328
 
1.8%
10 319
 
1.8%
Other values (134) 4259
23.4%
ValueCountFrequency (%)
0 241
 
1.3%
1 4900
26.9%
2 2827
15.5%
3 1857
 
10.2%
4 1255
 
6.9%
5 869
 
4.8%
6 682
 
3.7%
7 488
 
2.7%
8 423
 
2.3%
9 328
 
1.8%
ValueCountFrequency (%)
565 1
 
< 0.1%
455 1
 
< 0.1%
420 1
 
< 0.1%
405 1
 
< 0.1%
380 1
 
< 0.1%
355 3
< 0.1%
340 2
< 0.1%
315 3
< 0.1%
300 1
 
< 0.1%
290 1
 
< 0.1%

Preferred Foot
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Right
13996 
Left
4211 

Length

Max length5
Median length5
Mean length4.7687153
Min length4

Characters and Unicode

Total characters86824
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLeft
2nd rowRight
3rd rowRight
4th rowRight
5th rowRight

Common Values

ValueCountFrequency (%)
Right 13996
76.9%
Left 4211
 
23.1%

Length

2025-01-02T16:29:21.035881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T16:29:21.257704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
right 13996
76.9%
left 4211
 
23.1%

Most occurring characters

ValueCountFrequency (%)
t 18207
21.0%
R 13996
16.1%
i 13996
16.1%
g 13996
16.1%
h 13996
16.1%
L 4211
 
4.9%
e 4211
 
4.9%
f 4211
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86824
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 18207
21.0%
R 13996
16.1%
i 13996
16.1%
g 13996
16.1%
h 13996
16.1%
L 4211
 
4.9%
e 4211
 
4.9%
f 4211
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86824
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 18207
21.0%
R 13996
16.1%
i 13996
16.1%
g 13996
16.1%
h 13996
16.1%
L 4211
 
4.9%
e 4211
 
4.9%
f 4211
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86824
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 18207
21.0%
R 13996
16.1%
i 13996
16.1%
g 13996
16.1%
h 13996
16.1%
L 4211
 
4.9%
e 4211
 
4.9%
f 4211
 
4.9%

International Reputation
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing48
Missing (%)0.3%
Memory size1.0 MiB
1.0
16532 
2.0
 
1261
3.0
 
309
4.0
 
51
5.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54477
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
1.0 16532
90.8%
2.0 1261
 
6.9%
3.0 309
 
1.7%
4.0 51
 
0.3%
5.0 6
 
< 0.1%
(Missing) 48
 
0.3%

Length

2025-01-02T16:29:21.481426image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T16:29:21.948219image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 16532
91.0%
2.0 1261
 
6.9%
3.0 309
 
1.7%
4.0 51
 
0.3%
5.0 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 18159
33.3%
0 18159
33.3%
1 16532
30.3%
2 1261
 
2.3%
3 309
 
0.6%
4 51
 
0.1%
5 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54477
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 18159
33.3%
0 18159
33.3%
1 16532
30.3%
2 1261
 
2.3%
3 309
 
0.6%
4 51
 
0.1%
5 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54477
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 18159
33.3%
0 18159
33.3%
1 16532
30.3%
2 1261
 
2.3%
3 309
 
0.6%
4 51
 
0.1%
5 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54477
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 18159
33.3%
0 18159
33.3%
1 16532
30.3%
2 1261
 
2.3%
3 309
 
0.6%
4 51
 
0.1%
5 6
 
< 0.1%

Skill Moves
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing48
Missing (%)0.3%
Memory size1.0 MiB
2.0
8565 
3.0
6600 
1.0
2026 
4.0
917 
5.0
 
51

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54477
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row5.0
3rd row5.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
2.0 8565
47.0%
3.0 6600
36.2%
1.0 2026
 
11.1%
4.0 917
 
5.0%
5.0 51
 
0.3%
(Missing) 48
 
0.3%

Length

2025-01-02T16:29:22.201680image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T16:29:22.473543image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 8565
47.2%
3.0 6600
36.3%
1.0 2026
 
11.2%
4.0 917
 
5.0%
5.0 51
 
0.3%

Most occurring characters

ValueCountFrequency (%)
. 18159
33.3%
0 18159
33.3%
2 8565
15.7%
3 6600
 
12.1%
1 2026
 
3.7%
4 917
 
1.7%
5 51
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54477
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 18159
33.3%
0 18159
33.3%
2 8565
15.7%
3 6600
 
12.1%
1 2026
 
3.7%
4 917
 
1.7%
5 51
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54477
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 18159
33.3%
0 18159
33.3%
2 8565
15.7%
3 6600
 
12.1%
1 2026
 
3.7%
4 917
 
1.7%
5 51
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54477
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 18159
33.3%
0 18159
33.3%
2 8565
15.7%
3 6600
 
12.1%
1 2026
 
3.7%
4 917
 
1.7%
5 51
 
0.1%

Position
Categorical

High correlation 

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
ST
2154 
GK
2027 
CB
1779 
CM
1395 
LB
1372 
Other values (22)
9480 

Length

Max length3
Median length2
Mean length2.2581974
Min length2

Characters and Unicode

Total characters41115
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRF
2nd rowST
3rd rowLW
4th rowGK
5th rowRCM

Common Values

ValueCountFrequency (%)
ST 2154
11.8%
GK 2027
11.1%
CB 1779
9.8%
CM 1395
 
7.7%
LB 1372
 
7.5%
RB 1291
 
7.1%
RM 1127
 
6.2%
LM 1095
 
6.0%
CAM 959
 
5.3%
CDM 948
 
5.2%
Other values (17) 4060
22.3%

Length

2025-01-02T16:29:22.756106image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st 2154
11.8%
gk 2027
11.1%
cb 1779
9.8%
cm 1395
 
7.7%
lb 1372
 
7.5%
rb 1291
 
7.1%
rm 1127
 
6.2%
lm 1095
 
6.0%
cam 959
 
5.3%
cdm 948
 
5.2%
Other values (17) 4060
22.3%

Most occurring characters

ValueCountFrequency (%)
C 7251
17.6%
M 6843
16.6%
B 5917
14.4%
L 4455
10.8%
R 4416
10.7%
S 2564
 
6.2%
T 2154
 
5.2%
G 2027
 
4.9%
K 2027
 
4.9%
D 1439
 
3.5%
Other values (3) 2022
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41115
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 7251
17.6%
M 6843
16.6%
B 5917
14.4%
L 4455
10.8%
R 4416
10.7%
S 2564
 
6.2%
T 2154
 
5.2%
G 2027
 
4.9%
K 2027
 
4.9%
D 1439
 
3.5%
Other values (3) 2022
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41115
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 7251
17.6%
M 6843
16.6%
B 5917
14.4%
L 4455
10.8%
R 4416
10.7%
S 2564
 
6.2%
T 2154
 
5.2%
G 2027
 
4.9%
K 2027
 
4.9%
D 1439
 
3.5%
Other values (3) 2022
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41115
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 7251
17.6%
M 6843
16.6%
B 5917
14.4%
L 4455
10.8%
R 4416
10.7%
S 2564
 
6.2%
T 2154
 
5.2%
G 2027
 
4.9%
K 2027
 
4.9%
D 1439
 
3.5%
Other values (3) 2022
 
4.9%

Joined
Real number (ℝ)

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.4206
Minimum1991
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.4 KiB
2025-01-02T16:29:23.001527image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1991
5-th percentile2012
Q12016
median2017
Q32018
95-th percentile2018
Maximum2018
Range27
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.018194
Coefficient of variation (CV)0.0010008795
Kurtosis10.978378
Mean2016.4206
Median Absolute Deviation (MAD)1
Skewness-2.5756363
Sum36712970
Variance4.0731071
MonotonicityNot monotonic
2025-01-02T16:29:23.270966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2018 6569
36.1%
2017 4307
23.7%
2016 3799
20.9%
2015 1336
 
7.3%
2014 818
 
4.5%
2013 458
 
2.5%
2012 340
 
1.9%
2011 201
 
1.1%
2010 131
 
0.7%
2009 78
 
0.4%
Other values (12) 170
 
0.9%
ValueCountFrequency (%)
1991 1
 
< 0.1%
1998 3
 
< 0.1%
1999 1
 
< 0.1%
2000 2
 
< 0.1%
2001 2
 
< 0.1%
2002 10
0.1%
2003 13
0.1%
2004 12
0.1%
2005 17
0.1%
2006 18
0.1%
ValueCountFrequency (%)
2018 6569
36.1%
2017 4307
23.7%
2016 3799
20.9%
2015 1336
 
7.3%
2014 818
 
4.5%
2013 458
 
2.5%
2012 340
 
1.9%
2011 201
 
1.1%
2010 131
 
0.7%
2009 78
 
0.4%

Contract Valid Until
Date

Missing 

Distinct35
Distinct (%)0.2%
Missing289
Missing (%)1.6%
Memory size142.4 KiB
Minimum2018-01-01 00:00:00
Maximum2026-01-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-02T16:29:23.522484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:29:23.815677image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)

Height
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9467711
Minimum5.0833333
Maximum6.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.4 KiB
2025-01-02T16:29:24.050966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum5.0833333
5-th percentile5.5833333
Q15.75
median5.9166667
Q36.0833333
95-th percentile6.3333333
Maximum6.75
Range1.6666667
Interquartile range (IQR)0.33333333

Descriptive statistics

Standard deviation0.22051403
Coefficient of variation (CV)0.037081304
Kurtosis-0.22145276
Mean5.9467711
Median Absolute Deviation (MAD)0.16666667
Skewness-0.015476347
Sum108272.86
Variance0.048626436
MonotonicityNot monotonic
2025-01-02T16:29:24.310586image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
6 2881
15.8%
5.833333333 2479
13.6%
5.75 2238
12.3%
5.916666667 2159
11.9%
6.166666667 2015
11.1%
6.083333333 1908
10.5%
6.25 990
 
5.4%
5.666666667 946
 
5.2%
5.583333333 905
 
5.0%
6.333333333 749
 
4.1%
Other values (12) 937
 
5.1%
ValueCountFrequency (%)
5.083333333 3
 
< 0.1%
5.166666667 5
 
< 0.1%
5.25 18
 
0.1%
5.333333333 30
 
0.2%
5.416666667 145
 
0.8%
5.5 316
 
1.7%
5.583333333 905
 
5.0%
5.666666667 946
 
5.2%
5.75 2238
12.3%
5.833333333 2479
13.6%
ValueCountFrequency (%)
6.75 2
 
< 0.1%
6.666666667 10
 
0.1%
6.583333333 21
 
0.1%
6.5 93
 
0.5%
6.416666667 246
 
1.4%
6.333333333 749
 
4.1%
6.25 990
 
5.4%
6.166666667 2015
11.1%
6.083333333 1908
10.5%
6 2881
15.8%

Weight
Real number (ℝ)

High correlation 

Distinct58
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.97913
Minimum110
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.4 KiB
2025-01-02T16:29:24.595135image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile141
Q1154
median165
Q3176
95-th percentile194
Maximum243
Range133
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.572775
Coefficient of variation (CV)0.093823694
Kurtosis0.099552468
Mean165.97913
Median Absolute Deviation (MAD)11
Skewness0.21687243
Sum3021982
Variance242.51132
MonotonicityNot monotonic
2025-01-02T16:29:24.922630image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165 1483
 
8.1%
154 1439
 
7.9%
176 1041
 
5.7%
172 987
 
5.4%
159 946
 
5.2%
161 936
 
5.1%
163 901
 
4.9%
170 860
 
4.7%
168 836
 
4.6%
174 705
 
3.9%
Other values (48) 8073
44.3%
ValueCountFrequency (%)
110 2
 
< 0.1%
115 1
 
< 0.1%
117 6
 
< 0.1%
119 4
 
< 0.1%
121 10
 
0.1%
123 18
 
0.1%
126 14
 
0.1%
128 31
 
0.2%
130 33
 
0.2%
132 127
0.7%
ValueCountFrequency (%)
243 1
 
< 0.1%
236 2
 
< 0.1%
234 1
 
< 0.1%
229 1
 
< 0.1%
227 2
 
< 0.1%
225 3
 
< 0.1%
223 3
 
< 0.1%
220 1
 
< 0.1%
218 5
< 0.1%
216 9
< 0.1%

Release Clause
Real number (ℝ)

High correlation 

Distinct1245
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4585.061
Minimum13
Maximum228100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.4 KiB
2025-01-02T16:29:25.235712image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile179.3
Q1570
median1300
Q34585.0608
95-th percentile18600
Maximum228100
Range228087
Interquartile range (IQR)4015.0608

Descriptive statistics

Standard deviation10630.414
Coefficient of variation (CV)2.3184892
Kurtosis84.675754
Mean4585.061
Median Absolute Deviation (MAD)987
Skewness7.4363962
Sum83480205
Variance1.1300571 × 108
MonotonicityNot monotonic
2025-01-02T16:29:25.539397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4585.060806 1564
 
8.6%
1100 557
 
3.1%
1300 423
 
2.3%
1400 386
 
2.1%
1200 385
 
2.1%
1500 337
 
1.9%
1600 318
 
1.7%
1000 286
 
1.6%
1800 246
 
1.4%
1700 216
 
1.2%
Other values (1235) 13489
74.1%
ValueCountFrequency (%)
13 5
< 0.1%
15 3
 
< 0.1%
17 1
 
< 0.1%
18 4
 
< 0.1%
22 2
 
< 0.1%
25 10
0.1%
27 1
 
< 0.1%
29 1
 
< 0.1%
31 1
 
< 0.1%
32 1
 
< 0.1%
ValueCountFrequency (%)
228100 1
< 0.1%
226500 1
< 0.1%
196400 1
< 0.1%
172100 1
< 0.1%
166100 1
< 0.1%
165800 1
< 0.1%
164000 1
< 0.1%
160700 1
< 0.1%
156800 1
< 0.1%
156200 1
< 0.1%

Interactions

2025-01-02T16:29:07.379304image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:28:44.214769image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:28:46.840410image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:28:49.858968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:28:52.585514image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:28:54.814601image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:28:57.006826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:28:59.171597image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:29:01.372295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:29:04.657841image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:29:07.582217image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:28:44.422678image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:28:47.077833image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:28:50.184141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:28:52.790532image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:28:55.027313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:28:57.217806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:28:59.384352image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:29:01.650389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:29:05.014971image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:29:08.352051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:28:44.640446image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:28:47.325166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-02T16:28:50.552085image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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Correlations

2025-01-02T16:29:25.753806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AgeHeightIDInternational ReputationJoinedOverallPositionPotentialPreferred FootRelease ClauseSkill MovesValueWageWeight
Age1.0000.087-0.7610.140-0.1110.4830.089-0.2630.0220.1430.1010.2260.3220.230
Height0.0871.000-0.0960.016-0.0010.0380.207-0.0100.081-0.0090.230-0.0100.0180.755
ID-0.761-0.0961.0000.1970.195-0.5400.0690.0250.016-0.2860.107-0.355-0.421-0.202
International Reputation0.1400.0160.1971.0000.0830.5050.0800.2930.0000.4530.1800.4800.5050.045
Joined-0.111-0.0010.1950.0831.000-0.1510.041-0.0920.021-0.1610.039-0.125-0.119-0.016
Overall0.4830.038-0.5400.505-0.1511.0000.0960.6250.0560.8540.2900.9370.7780.147
Position0.0890.2070.0690.0800.0410.0961.0000.0650.4860.0840.5710.1210.0990.185
Potential-0.263-0.0100.0250.293-0.0920.6250.0651.0000.0390.7760.2300.7700.535-0.011
Preferred Foot0.0220.0810.0160.0000.0210.0560.4860.0391.0000.0000.1200.0000.0000.072
Release Clause0.143-0.009-0.2860.453-0.1610.8540.0840.7760.0001.0000.1900.9450.7210.049
Skill Moves0.1010.2300.1070.1800.0390.2900.5710.2300.1200.1901.0000.1950.1420.197
Value0.226-0.010-0.3550.480-0.1250.9370.1210.7700.0000.9450.1951.0000.7760.068
Wage0.3220.018-0.4210.505-0.1190.7780.0990.5350.0000.7210.1420.7761.0000.084
Weight0.2300.755-0.2020.045-0.0160.1470.185-0.0110.0720.0490.1970.0680.0841.000

Missing values

2025-01-02T16:29:10.179479image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-02T16:29:10.744560image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-02T16:29:11.152016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDNameAgeNationalityOverallPotentialClubValueWagePreferred FootInternational ReputationSkill MovesPositionJoinedContract Valid UntilHeightWeightRelease Clause
0158023L. Messi31Argentina9494FC Barcelona110500.0565.0Left5.04.0RF20042021-01-015.583333159.0226500.0
120801Cristiano Ronaldo33Portugal9494Juventus77000.0405.0Right5.05.0ST20182022-01-016.166667183.0127100.0
2190871Neymar Jr26Brazil9293Paris Saint-Germain118500.0290.0Right5.05.0LW20172022-01-015.750000150.0228100.0
3193080De Gea27Spain9193Manchester United72000.0260.0Right4.01.0GK20112020-01-016.333333168.0138600.0
4192985K. De Bruyne27Belgium9192Manchester City102000.0355.0Right4.04.0RCM20152023-01-015.916667154.0196400.0
5183277E. Hazard27Belgium9191Chelsea93000.0340.0Right4.04.0LF20122020-01-015.666667163.0172100.0
6177003L. Modrić32Croatia9191Real Madrid67000.0420.0Right4.04.0RCM20122020-01-015.666667146.0137400.0
7176580L. Suárez31Uruguay9191FC Barcelona80000.0455.0Right5.03.0RS20142021-01-016.000000190.0164000.0
8155862Sergio Ramos32Spain9191Real Madrid51000.0380.0Right4.03.0RCB20052020-01-016.000000181.0104600.0
9200389J. Oblak25Slovenia9093Atlético Madrid68000.094.0Right3.01.0GK20142021-01-016.166667192.0144500.0
IDNameAgeNationalityOverallPotentialClubValueWagePreferred FootInternational ReputationSkill MovesPositionJoinedContract Valid UntilHeightWeightRelease Clause
18197246167D. Holland18Republic of Ireland4761Cork City60.01.0Right1.02.0CM20182018-01-015.833333141.088.0
18198242844J. Livesey18England4770Burton Albion60.01.0Right1.01.0GK20182021-01-015.916667154.0165.0
18199244677M. Baldisimo18Canada4769Vancouver Whitecaps FC70.01.0Right1.02.0CM20182021-01-015.500000150.0175.0
18200231381J. Young18Scotland4762Swindon Town60.01.0Left1.02.0ST20152019-01-015.750000157.0143.0
18201243413D. Walsh18Republic of Ireland4768Waterford FC60.01.0Left1.02.0RB20182018-01-016.083333168.0153.0
18202238813J. Lundstram19England4765Crewe Alexandra60.01.0Right1.02.0CM20172019-01-015.750000134.0143.0
18203243165N. Christoffersson19Sweden4763Trelleborgs FF60.01.0Right1.02.0ST20182020-01-016.250000170.0113.0
18204241638B. Worman16England4767Cambridge United60.01.0Right1.02.0ST20172021-01-015.666667148.0165.0
18205246268D. Walker-Rice17England4766Tranmere Rovers60.01.0Right1.02.0RW20182019-01-015.833333154.0143.0
18206246269G. Nugent16England4666Tranmere Rovers60.01.0Right1.02.0CM20182019-01-015.833333176.0165.0